{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3eae835d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f0246c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6a29829",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"data/credit-a.csv\",header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "759f2825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>560.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>280</td>\n",
       "      <td>824.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0      1      2   3   4   5   6     7   8   9   10  11  12   13     14  15\n",
       "0   0  30.83  0.000   0   0   9   0  1.25   0   0   1   1   0  202    0.0  -1\n",
       "1   1  58.67  4.460   0   0   8   1  3.04   0   0   6   1   0   43  560.0  -1\n",
       "2   1  24.50  0.500   0   0   8   1  1.50   0   1   0   1   0  280  824.0  -1\n",
       "3   0  27.83  1.540   0   0   9   0  3.75   0   0   5   0   0  100    3.0  -1\n",
       "4   0  20.17  5.625   0   0   9   0  1.71   0   1   0   1   2  120    0.0  -1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba7ccbbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    357\n",
       "-1    296\n",
       "Name: 15, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:,-1].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47d34485",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.iloc[:,:-1]\n",
    "y=data.iloc[:,-1].replace(-1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "929d7569",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=tf.keras.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d0064b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(5,input_shape=(15,),activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7070a8fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(5,activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "458535eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "61fe6aa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 5)                 80        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 5)                 30        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 116\n",
      "Trainable params: 116\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2111ba4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "             loss=\"binary_crossentropy\",\n",
    "              #loss=\"mse\",\n",
    "             metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb062b50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "21/21 [==============================] - 1s 2ms/step - loss: 6.1283 - acc: 0.6837\n",
      "Epoch 2/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 6.9303 - acc: 0.6519\n",
      "Epoch 3/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 3.1000 - acc: 0.6852\n",
      "Epoch 4/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 2.4877 - acc: 0.6498\n",
      "Epoch 5/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 1.0240 - acc: 0.6688\n",
      "Epoch 6/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.8583 - acc: 0.6730\n",
      "Epoch 7/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.7595 - acc: 0.6618\n",
      "Epoch 8/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.7810 - acc: 0.6798\n",
      "Epoch 9/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.8137 - acc: 0.6830\n",
      "Epoch 10/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6674 - acc: 0.6840\n",
      "Epoch 11/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.7434 - acc: 0.6485\n",
      "Epoch 12/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.9708 - acc: 0.6796\n",
      "Epoch 13/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.7844 - acc: 0.6450\n",
      "Epoch 14/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6957 - acc: 0.6908\n",
      "Epoch 15/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6846 - acc: 0.6622\n",
      "Epoch 16/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6712 - acc: 0.6802\n",
      "Epoch 17/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6735 - acc: 0.6582\n",
      "Epoch 18/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6428 - acc: 0.6757\n",
      "Epoch 19/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6394 - acc: 0.7034\n",
      "Epoch 20/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6508 - acc: 0.6849\n",
      "Epoch 21/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6492 - acc: 0.6572\n",
      "Epoch 22/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6162 - acc: 0.6833\n",
      "Epoch 23/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6109 - acc: 0.6930\n",
      "Epoch 24/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6020 - acc: 0.7172\n",
      "Epoch 25/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6198 - acc: 0.6800\n",
      "Epoch 26/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6308 - acc: 0.6553\n",
      "Epoch 27/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5989 - acc: 0.6994\n",
      "Epoch 28/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6277 - acc: 0.6802\n",
      "Epoch 29/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6226 - acc: 0.6897\n",
      "Epoch 30/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6391 - acc: 0.6648\n",
      "Epoch 31/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6267 - acc: 0.6840\n",
      "Epoch 32/100\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.6163 - acc: 0.6912\n",
      "Epoch 33/100\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.6593 - acc: 0.6574\n",
      "Epoch 34/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6217 - acc: 0.6870\n",
      "Epoch 35/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6033 - acc: 0.6989\n",
      "Epoch 36/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6066 - acc: 0.7025\n",
      "Epoch 37/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6162 - acc: 0.6721\n",
      "Epoch 38/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6322 - acc: 0.6917\n",
      "Epoch 39/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6567 - acc: 0.6757\n",
      "Epoch 40/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6219 - acc: 0.6824\n",
      "Epoch 41/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6231 - acc: 0.6715\n",
      "Epoch 42/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6448 - acc: 0.6629\n",
      "Epoch 43/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.6281 - acc: 0.6979\n",
      "Epoch 44/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6235 - acc: 0.6863\n",
      "Epoch 45/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6096 - acc: 0.6643\n",
      "Epoch 46/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5964 - acc: 0.7194\n",
      "Epoch 47/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6277 - acc: 0.6551\n",
      "Epoch 48/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6227 - acc: 0.6829\n",
      "Epoch 49/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6259 - acc: 0.6711\n",
      "Epoch 50/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5979 - acc: 0.7002\n",
      "Epoch 51/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5992 - acc: 0.7038\n",
      "Epoch 52/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5972 - acc: 0.7142\n",
      "Epoch 53/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6213 - acc: 0.6640\n",
      "Epoch 54/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6175 - acc: 0.7029\n",
      "Epoch 55/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6038 - acc: 0.6941\n",
      "Epoch 56/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5995 - acc: 0.6843\n",
      "Epoch 57/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6318 - acc: 0.6648\n",
      "Epoch 58/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6284 - acc: 0.6808\n",
      "Epoch 59/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6186 - acc: 0.6773\n",
      "Epoch 60/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5992 - acc: 0.6968\n",
      "Epoch 61/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6171 - acc: 0.6720\n",
      "Epoch 62/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6372 - acc: 0.6658\n",
      "Epoch 63/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6057 - acc: 0.7063\n",
      "Epoch 64/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5957 - acc: 0.7047\n",
      "Epoch 65/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6175 - acc: 0.6856\n",
      "Epoch 66/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6018 - acc: 0.6985\n",
      "Epoch 67/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6270 - acc: 0.6753\n",
      "Epoch 68/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5959 - acc: 0.7209\n",
      "Epoch 69/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5976 - acc: 0.6881\n",
      "Epoch 70/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6277 - acc: 0.6741\n",
      "Epoch 71/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6222 - acc: 0.6764\n",
      "Epoch 72/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6030 - acc: 0.6885\n",
      "Epoch 73/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6171 - acc: 0.6768\n",
      "Epoch 74/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5972 - acc: 0.7070\n",
      "Epoch 75/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6048 - acc: 0.6993\n",
      "Epoch 76/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6338 - acc: 0.6815\n",
      "Epoch 77/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6093 - acc: 0.6977\n",
      "Epoch 78/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6129 - acc: 0.7023\n",
      "Epoch 79/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6125 - acc: 0.6733\n",
      "Epoch 80/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5833 - acc: 0.7216\n",
      "Epoch 81/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6012 - acc: 0.6994\n",
      "Epoch 82/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6077 - acc: 0.6888\n",
      "Epoch 83/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6078 - acc: 0.6925\n",
      "Epoch 84/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6181 - acc: 0.6862\n",
      "Epoch 85/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6025 - acc: 0.7084\n",
      "Epoch 86/100\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.6076 - acc: 0.6908\n",
      "Epoch 87/100\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.6033 - acc: 0.7047\n",
      "Epoch 88/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6214 - acc: 0.6946\n",
      "Epoch 89/100\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.5940 - acc: 0.7056\n",
      "Epoch 90/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6381 - acc: 0.6609\n",
      "Epoch 91/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6233 - acc: 0.7016\n",
      "Epoch 92/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6237 - acc: 0.6826\n",
      "Epoch 93/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6058 - acc: 0.6762\n",
      "Epoch 94/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5927 - acc: 0.7001\n",
      "Epoch 95/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6433 - acc: 0.6761\n",
      "Epoch 96/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.6106 - acc: 0.6716\n",
      "Epoch 97/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5685 - acc: 0.7411\n",
      "Epoch 98/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5967 - acc: 0.6973\n",
      "Epoch 99/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6177 - acc: 0.6748\n",
      "Epoch 100/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5837 - acc: 0.7185\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(x,y,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f5833d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'acc'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d5e78f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe6bcb7dc10>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch,history.history.get('loss'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "76df8e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe6bca5c340>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch,history.history.get('acc'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c19dcd21",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
